Freeland Bunker Analysis
2023-07-28
#chooseCRANmirror(ind=70)
#install.packages("readxl")
library(readxl)
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.2 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.2 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(tidytext)
library(ggplot2)
library(stringr)
library(dplyr)
library(textclean)
library(stopwords)
library(wordcloud2)
library(RColorBrewer)
library(wordcloud)
library(forcats)Read Excel Freeland Bunker Journal Transcription (1873-1874)
journal_1873 <- read_excel("data/journal_1873.xlsx")
journal_1874 <- read_excel("data/journal_1874.xlsx")
names_journal_1873 <- read_excel("data/names_journal_1873.xlsx")
names_journal_1874 <- read_excel("data/names_journal_1874.xlsx")#The column name for this journal only was originally date --> the journal has been updated in drive
journal_1873 <- journal_1873 %>%
rename(month = date)# I corrected some typos in journal_entry and in the letter column when create the names_journal_187, which is why I am keeping these columns from names_journal_1873
# The current 1873 and 1874 journals have been updated in drive, there should be no future need to join datasets
journal_1873 <- left_join(names_journal_1873, journal_1873, by = c("date_mdy", "month", "journal_entry", "letter"))#I corrected some typos in journal_entry and in the letter column when create the names_journal_1874, which is why I am keeping these columns from names_journal_1874
journal_1874 <- left_join(names_journal_1874, journal_1874, by = c("date_mdy", "month", "journal_entry", "letter"))# There was a typo in the name of the "transcription_accuracy column, it has been updated in the drive
colnames(journal_1874)[13] <- "transcription_accuracy"
combined_journals <- rbind(journal_1873, journal_1874)Exploratory Visualizations
Wind Speed 1873
Categories might need to be redefined and given levels to be more meaningful
wind_speed_1873 <- journal_1873 %>%
select(date_mdy,
month,
wind_speed_am,
wind_speed_pm,
wind_speed_night) %>%
mutate(year = "1873") %>%
pivot_longer(cols = starts_with("wind_speed"),
names_to = "period",
values_to = "wind_speed") %>%
mutate(period = case_when(
period == "wind_speed_am" ~ "am",
period == "wind_speed_pm" ~ "pm",
period == "wind_speed_night" ~ "night")) %>%
separate_rows(wind_speed, sep = ",") %>%
mutate(category = case_when(
wind_speed %in% c("blustering", "very blustering") ~ "blustering",
wind_speed %in% c("breezy", "good breeze", "very fresh breeze", "breezed up", "fresh breeze", "heavy breeze", "smart breeze", "strong breeze") ~ "breeze",
wind_speed %in% c("blowing very heavy", "fresh blow", "heavy blow", "very heavy blow", "blowy", "blowing", "blowing hard", "blowing very hard") ~ "blow",
wind_speed %in% c("fresh gale", "gale") ~ "gale",
wind_speed %in% c("moderate", "quite moderate", "very moderate") ~ "moderate",
wind_speed %in% c("pleasant", "quite pleasant", "very pleasant") ~ "pleasant",
wind_speed %in% c("calm", "perfectly calm") ~ "calm",
wind_speed %in% c("scant wind", "heavy winds") ~ "wind",
wind_speed %in% c("rough", "squall", "strong", "very light", "heavy", "baffling", "a light air", "variable", "fair") ~ "other intensities"
))wind_speed_1873 %>%
mutate(wind_speed = ifelse(wind_speed == "NA", NA, wind_speed)) %>%
drop_na(wind_speed, period, category) %>%
group_by(wind_speed, period, category) %>%
summarize(n = n()) %>%
mutate(period = factor(period, levels = c("am", "pm", "night"))) %>%
ggplot(aes(x = wind_speed,
y = n,
fill = category)) +
geom_bar(stat = "identity",
position = "dodge") +
labs(title = "Wind Vocabulary Frequency by Period of the Day 1873",
x = "Wind Speed Vocabulary",
y = "Frequency",
fill = "Category") +
theme_minimal() +
coord_flip() +
facet_wrap(~ period)## `summarise()` has grouped output by 'wind_speed', 'period'. You can override
## using the `.groups` argument.
wind_speed_1873 %>%
mutate(wind_speed = ifelse(wind_speed == "NA", NA, wind_speed)) %>%
drop_na(wind_speed, period, category) %>%
group_by(wind_speed, period, category) %>%
summarize(n = n()) %>%
mutate(period = factor(period, levels = c("am", "pm", "night"))) %>%
ggplot(aes(x = wind_speed,
y = n,
fill = category)) +
geom_bar(stat = "identity",
position = "dodge") +
labs(title = "General Wind Vocabulary Frequency 1873",
x = "Wind Speed Vocabulary",
y = "Frequency",
fill = "Category") +
theme_minimal(base_size = 7) +
coord_polar() +
scale_y_log10() # to better see smaller frequencies## `summarise()` has grouped output by 'wind_speed', 'period'. You can override
## using the `.groups` argument.
wind_speed_1873 %>%
mutate(wind_speed = ifelse(wind_speed == "NA", NA, wind_speed)) %>%
drop_na(wind_speed, period, category) %>%
group_by(wind_speed, period, category) %>%
summarize(n = n()) %>%
mutate(period = factor(period, levels = c("am", "pm", "night"))) %>%
ggplot(aes(x = "", y = n, fill = category)) +
geom_bar(stat = "identity", width = 1) + # 1 for pie chart effect
labs(x = NULL, y = "Frequency", fill = "Category") +
theme_minimal() +
coord_polar("y", start = 0) +
theme_void() +
labs(title = " 1873 Wind Vocabulary by Category")## `summarise()` has grouped output by 'wind_speed', 'period'. You can override
## using the `.groups` argument.
Wind Speed Comparison 1873-1874
Categories might need to be redefined and given levels to be more meaningful
wind_speed_1874 <- journal_1874 %>%
select(date_mdy,
month,
wind_speed_am,
wind_speed_pm,
wind_speed_night) %>%
mutate(year = 1874) %>%
pivot_longer(cols = starts_with("wind_speed"),
names_to = "period",
values_to = "wind_speed") %>%
mutate(period = case_when(
period == "wind_speed_am" ~ "am",
period == "wind_speed_pm" ~ "pm",
period == "wind_speed_night" ~ "night")) %>%
separate_rows(wind_speed, sep = ",") %>%
mutate(category = case_when(
wind_speed %in% c("blustering", "very blustering") ~ "blustering",
wind_speed %in% c("breezy", "good breeze", "very fresh breeze", "breezed up", "fresh breeze", "heavy breeze", "smart breeze", "strong breeze") ~ "breeze",
wind_speed %in% c("blowing very heavy", "fresh blow", "heavy blow", "very heavy blow", "blowy", "blowing", "blowing hard", "blowing very hard") ~ "blow",
wind_speed %in% c("fresh gale", "gale") ~ "gale",
wind_speed %in% c("moderate", "quite moderate", "very moderate") ~ "moderate",
wind_speed %in% c("pleasant", "quite pleasant", "very pleasant") ~ "pleasant",
wind_speed %in% c("calm", "perfectly calm") ~ "calm",
wind_speed %in% c("scant wind", "heavy winds") ~ "wind",
wind_speed %in% c("rough", "squall", "strong", "very light", "heavy", "baffling", "a light air", "variable", "fair") ~ "other intensities"
))combined_wind_speed<- rbind(wind_speed_1873, wind_speed_1874)combined_wind_speed %>%
mutate(wind_speed = ifelse(wind_speed == "NA", NA, wind_speed)) %>%
drop_na(wind_speed, period, category, year) %>%
group_by(wind_speed, category, year) %>%
summarize(n = n()) %>%
ggplot(aes(x = wind_speed, y = n, fill = category)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Yearly Comparison of Wind Speed Vocabulary",
subtitle = "Colored by Overarching Categories in 1873 and 1874",
x = "Wind Speed Vocabulary",
y = "Frequency",
fill = "Category") +
theme_minimal() +
coord_flip() +
facet_wrap(~ year)## `summarise()` has grouped output by 'wind_speed', 'category'. You can override
## using the `.groups` argument.
combined_wind_speed %>%
mutate(wind_speed = ifelse(wind_speed == "NA", NA, wind_speed)) %>%
drop_na(wind_speed, period, category, year) %>%
group_by(category, month, year) %>%
summarise(frequency = n()) %>%
ggplot(aes(x = category, y = frequency, fill = as.factor(year))) +
geom_bar(stat = "identity", position = "stack") +
labs(title = "Monthly Comparison of Wind Speed Categories 1873-1874",
x = "Category",
y = "Frequency",
fill = "Year") +
facet_wrap(~ month) +
coord_flip()## `summarise()` has grouped output by 'category', 'month'. You can override using
## the `.groups` argument.
Wind Directions 1873
convert_wind_direction_to_degrees <- function(wind_direction) {
degrees <- case_when(
wind_direction == "N" ~ 0,
wind_direction == "NNE" ~ 22.5,
wind_direction == "NE" ~ 45,
wind_direction == "ENE" ~ 67.5,
wind_direction == "E" ~ 90,
wind_direction == "ESE" ~ 112.5,
wind_direction == "SE" ~ 135,
wind_direction == "SSE" ~ 157.5,
wind_direction == "S" ~ 180,
wind_direction == "SSW" ~ 202.5,
wind_direction == "SW" ~ 225,
wind_direction == "WSW" ~ 247.5,
wind_direction == "W" ~ 270,
wind_direction == "WNW" ~ 292.5,
wind_direction == "NW" ~ 315,
wind_direction == "NNW" ~ 337.5,
TRUE ~ NA_real_ # Return NA if the wind direction is not recognized
)
return(degrees)
}wind_direction_1873_long <- journal_1873 %>%
pivot_longer(cols = starts_with("wind_direction"),
names_to = "period",
values_to = "wind_direction") %>%
separate_rows(wind_direction, sep = ", ") %>%
mutate(period = case_when(
period == "wind_direction_am" ~ "am",
period == "wind_direction_pm" ~ "pm",
period == "wind_direction_night" ~ "night"
))wind_direction_1873_long <- wind_direction_1873_long %>%
mutate(
wind_degrees = case_when(
period == "am" ~ convert_wind_direction_to_degrees(wind_direction),
period == "pm" ~ convert_wind_direction_to_degrees(wind_direction),
period == "night" ~ convert_wind_direction_to_degrees(wind_direction)
)) %>%
select(date_mdy, month, wind_direction, period, wind_degrees) %>%
mutate(year = "1873")# Function to convert degrees to radians for polar coordinates
to_radians <- function(degrees) {
return((degrees - 90) * pi / 180)
}
wind_direction_1873_long %>%
ggplot(aes(x = to_radians(wind_degrees))) +
geom_bar(aes(fill = stat(count)),
bins = 16,
color = "black") +
scale_fill_viridis_c(option = "plasma", name = "Frequency") +
geom_text(aes(x = to_radians(wind_degrees),
y = 150, label = wind_direction),
size = 4, fontface = "bold",
color = "black") +
labs(title = "Wind Directions Mentionned, 1873",
"Only Directions Explicitely Specified by Period",
x = NULL,
y = NULL) +
theme_minimal() +
theme(axis.text = element_blank()) +
coord_polar() +
scale_y_continuous(name = "Frequency",
trans = "log10") # Frequency scale (logarithmic)## Warning in geom_bar(aes(fill = stat(count)), bins = 16, color = "black"):
## Ignoring unknown parameters: `bins`## Warning: `stat(count)` was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(count)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning: Removed 761 rows containing non-finite values (`stat_count()`).## Warning: Removed 761 rows containing missing values (`geom_text()`).
wind_direction_1873_long %>%
mutate(period = factor(period, levels = c("am", "pm", "night"))) %>%
ggplot(aes(x = to_radians(wind_degrees))) +
geom_bar(aes(fill = stat(count)),
bins = 16,
color = "black") +
scale_fill_viridis_c(option = "plasma", name = "Frequency") +
geom_text(aes(x = to_radians(wind_degrees),
y = 150, label = wind_direction),
size = 4, fontface = "bold",
color = "black") +
labs(title = "Wind Directions Mentionned by Period of the Day, 1873",
x = NULL,
y = NULL) +
theme_minimal() +
theme(axis.text = element_blank()) +
coord_polar() +
scale_y_continuous(name = "Frequency",
trans = "log10") + # Frequency scale (logarithmic)
facet_wrap(~ period)## Warning in geom_bar(aes(fill = stat(count)), bins = 16, color = "black"):
## Ignoring unknown parameters: `bins`## Warning: Removed 761 rows containing non-finite values (`stat_count()`).## Warning: Removed 761 rows containing missing values (`geom_text()`).
wind_direction_1873_long %>%
ggplot(aes(x = to_radians(wind_degrees))) +
geom_bar(aes(fill = stat(count)),
bins = 16,
color = "black") +
scale_fill_viridis_c(option = "plasma", name = "Frequency") +
geom_text(aes(x = to_radians(wind_degrees),
y = 150, label = wind_direction),
size = 4, fontface = "bold",
color = "black") +
labs(title = "Wind Directions Mentioned by Month, 1873",
x = NULL,
y = NULL) +
theme_minimal() +
theme(axis.text = element_blank()) +
coord_polar() +
scale_y_continuous(name = "Frequency",
trans = "log10") + # Frequency scale (logarithmic)
facet_wrap(~ month)## Warning in geom_bar(aes(fill = stat(count)), bins = 16, color = "black"):
## Ignoring unknown parameters: `bins`## Warning: Removed 761 rows containing non-finite values (`stat_count()`).## Warning: Removed 761 rows containing missing values (`geom_text()`).
wind_direction_1873_long %>%
mutate(wind_direction = ifelse(wind_direction == "NA", NA, wind_direction)) %>%
mutate(period = factor(period, levels = c("am", "pm", "night"))) %>%
drop_na(wind_direction, period, month) %>%
ggplot(aes(x = wind_direction, fill = period)) +
geom_bar(position = "stack", color = "black") +
labs(title = "Bar Chart of Monthly Wind Directions Frequencies in 1873",
x = "Wind Direction",
y = "Frequency") +
coord_flip() +
facet_wrap(~ month, nrow = 2)
Wind Direction Comparison 1873-1874
wind_direction_1874_long <- journal_1874 %>%
pivot_longer(cols = starts_with("wind_direction"),
names_to = "period",
values_to = "wind_direction") %>%
separate_rows(wind_direction, sep = ", ") %>%
mutate(period = case_when(
period == "wind_direction_am" ~ "am",
period == "wind_direction_pm" ~ "pm",
period == "wind_direction_night" ~ "night"
))wind_direction_1874_long <- wind_direction_1874_long %>%
mutate(
wind_degrees = case_when(
period == "am" ~ convert_wind_direction_to_degrees(wind_direction),
period == "pm" ~ convert_wind_direction_to_degrees(wind_direction),
period == "night" ~ convert_wind_direction_to_degrees(wind_direction)
)) %>%
select(date_mdy, month, wind_direction, period, wind_degrees) %>%
mutate(year = "1874") combined_wind_direction <- rbind(wind_direction_1873_long, wind_direction_1874_long)combined_wind_direction %>%
mutate(wind_direction = ifelse(wind_direction == "NA", NA, wind_direction)) %>%
drop_na(wind_direction, period, year) %>%
ggplot(aes(x = wind_direction, fill = year)) +
geom_bar(position = "stack", color = "black") +
labs(title = "Bar Chart of Yearly Wind Directions Frequencies",
x = "Wind Direction",
y = "Frequency") +
coord_flip() 
Wind Direction and Wind Speed
Categories might need to be redefined and given levels to be more meaningful
combined_wind <- full_join(combined_wind_direction, combined_wind_speed, by = c("date_mdy", "month", "year", "period"))## Warning in full_join(combined_wind_direction, combined_wind_speed, by = c("date_mdy", : Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 94 of `x` matches multiple rows in `y`.
## ℹ Row 409 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
## "many-to-many"` to silence this warning.combined_wind <- combined_wind %>%
mutate(wind_direction = ifelse(wind_direction == "NA", NA, wind_direction)) %>%
mutate(wind_speed = ifelse(wind_speed == "NA", NA, wind_speed )) %>%
drop_na(wind_direction, wind_speed)freq_combined_wind <- combined_wind %>%
group_by(wind_speed,
wind_direction) %>%
summarize(frequency = n())## `summarise()` has grouped output by 'wind_speed'. You can override using the
## `.groups` argument.freq_combined_wind %>%
ggplot(aes(x = wind_direction,
y = wind_speed,
fill = frequency)) +
geom_tile() +
scale_fill_viridis_c() +
labs(title = "Wind Speed and Wind Direction Heat Map",
x = "Wind Direction",
y = "Wind Speed",
fill = "Frequency") +
scale_fill_viridis_c(direction = -1) #for darker = higehr frequency## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
combined_wind %>%
group_by(wind_speed,
wind_direction,
year) %>%
summarize(frequency = n()) %>%
ggplot(aes(x = wind_direction,
y = wind_speed,
fill = frequency)) +
geom_tile() +
scale_fill_viridis_c() +
labs(title = "Wind Speed and Wind Direction Heat Map by Year",
x = "Wind Direction",
y = "Wind Speed",
fill = "Frequency") +
scale_fill_viridis_c(direction = -1) +
facet_wrap(~ year)## `summarise()` has grouped output by 'wind_speed', 'wind_direction'. You can
## override using the `.groups` argument.
## Scale for fill is already present. Adding another scale for fill, which will
## replace the existing scale.
Weather Conditions
Categories might need to be redefined and given levels to be more meaningful
weather_con_1873_long <- journal_1873 %>%
select(date_mdy,
month,
weather_condition_am,
weather_condition_pm,
weather_condition_night) %>%
mutate(year = "1873") %>%
pivot_longer(cols = starts_with("weather_condition"),
names_to = "period",
values_to = "weather_condition") %>%
mutate(period = case_when(
period == "weather_condition_am" ~ "am",
period == "weather_condition_pm" ~ "pm",
period == "weather_condition_night" ~ "night")) %>%
separate_rows(weather_condition, sep = ",") %>%
mutate(category = case_when(
weather_condition %in% c("chilly", "cold", "cool", "extremely cold", "very cold", "quite cold") ~ "cold",
weather_condition %in% c("pleasant", "very pleasant") ~ "pleasant",
weather_condition %in% c("very warm", "warm", "hot") ~ "warm",
weather_condition %in% c("clear", "cleared up", "fine") ~ "clear",
weather_condition %in% c("overcast", "cloudy") ~ "cloud",
weather_condition %in% c( "pleasant", "quite pleasant", "very pleasant") ~ "pleasant",
weather_condition %in% c("calm", "perfectly calm") ~ "calm",
weather_condition %in% c("foggy", "foggy", "dense fog", "thick fog", "very foggy", "thick with fog", "very thick with fog") ~ "fog",
weather_condition %in% c("heavy showers", "very heavy shower", "shower", "showery", "a little showery") ~ "showers",
weather_condition %in% c("drizzle", "drizzling rain") ~ "drizzle",
weather_condition %in% c("cold rain", "heavy rain storm", "heavy rain", "fine rain", "raining", "rainy", "rain", "rain spells", "rain squall", "rain storm", "moderate rain", "big rain storm", "very heavy rain") ~ "rain",
weather_condition %in% c("little snow", "snow sleet", "light snow", "thick snow", "pleasant snow", "snowy", "snow", "snow spells", "snow squall", "big snow storm", "snow storm", "snowing", "snowing fast", "moderate snow") ~ "snow",
weather_condition %in% c("stormy", "tough storm", "very heavy storm", "moderate rainstorm", "heavy storm") ~ "storm",
weather_condition %in% c("thunder", "heavy thunder") ~ "thunder",
weather_condition %in% c("sharp lightning", "lightning") ~ "lightning",
weather_condition %in% c("misty", "good weather", "moderate weather", "sun out", "hail") ~ "other",
))weather_con_1873_freq <- weather_con_1873_long %>%
mutate(weather_condition = ifelse(weather_condition == "NA", NA, weather_condition)) %>%
drop_na(weather_condition) %>%
count(weather_condition)
category_colors <- c(
"cold" = "#16324a",
"pleasant" = "#ccf146",
"warm" = "#b5a642",
"clear" = "#9467bd",
"cloud" = "#8c564b",
"calm" = "#2ca02c",
"fog" = "#e377c2",
"showers" = "#1f77b4",
"drizzle" = "#004f95",
"rain" = "#17becf",
"snow" = "#bbbbbb",
"storm" = "#ff7f0e",
"thunder" = "#d62728",
"lightning" = "#ff9896",
"other" = "#c5b0d5"
)
wordcloud(
weather_con_1873_freq$weather_condition,
weather_con_1873_freq$n,
colors = category_colors, #this is not currently working
random.order = FALSE,
scale = c(5, 1),
min.freq = 1,
max.words = Inf
)
weather_con_1873_long %>%
mutate(weather_condition = ifelse(weather_condition == "NA", NA, weather_condition)) %>%
drop_na( weather_condition, period, category) %>%
group_by( weather_condition, period, category) %>%
summarize(n = n()) %>%
mutate(period = factor(period, levels = c("am", "pm", "night"))) %>%
ggplot(aes(x = weather_condition,
y = n,
fill = category)) +
geom_bar(stat = "identity",
position = "dodge") +
labs(title = "Weather Condition Frequency by Period of the Day 1873",
x = "Weather Condition Vocabulary",
y = "Frequency",
fill = "Category") +
theme_minimal() +
coord_flip() +
facet_wrap(~ period)## `summarise()` has grouped output by 'weather_condition', 'period'. You can
## override using the `.groups` argument.
weather_con_1873_long %>%
mutate(weather_condition = ifelse(weather_condition == "NA", NA, weather_condition)) %>%
drop_na(weather_condition, category, month) %>%
group_by(month) %>%
count(category) %>%
ggplot(aes(x = factor(month, levels = c("January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December")),
y = n,
fill = category )) +
geom_col(position = "stack") +
scale_fill_brewer(palette = "Spectral") +
labs(title = "Weather Frequency By Category per month",
fill = "Caterogy",
x = "Month",
y = "Number of occurrences") +
coord_polar()
weather_con_1874_long <- journal_1874 %>%
select(date_mdy,
month,
weather_condition_am,
weather_condition_pm,
weather_condition_night) %>%
mutate(year = "1874") %>%
pivot_longer(cols = starts_with("weather_condition"),
names_to = "period",
values_to = "weather_condition") %>%
mutate(period = case_when(
period == "weather_condition_am" ~ "am",
period == "weather_condition_pm" ~ "pm",
period == "weather_condition_night" ~ "night")) %>%
separate_rows(weather_condition, sep = ",") %>%
mutate(category = case_when(
weather_condition %in% c("chilly", "cold", "cool", "extremely cold", "very cold", "quite cold") ~ "cold",
weather_condition %in% c("pleasant", "very pleasant") ~ "pleasant",
weather_condition %in% c("very warm", "warm", "hot") ~ "warm",
weather_condition %in% c("clear", "cleared up", "fine") ~ "clear",
weather_condition %in% c("overcast", "cloudy") ~ "cloud",
weather_condition %in% c( "pleasant", "quite pleasant", "very pleasant") ~ "pleasant",
weather_condition %in% c("calm", "perfectly calm") ~ "calm",
weather_condition %in% c("foggy", "foggy", "dense fog", "thick fog", "very foggy", "thick with fog", "very thick with fog") ~ "fog",
weather_condition %in% c("heavy showers", "very heavy shower", "shower", "showery", "a little showery") ~ "showers",
weather_condition %in% c("drizzle", "drizzling rain") ~ "drizzle",
weather_condition %in% c("cold rain", "heavy rain storm", "heavy rain", "fine rain", "raining", "rainy", "rain", "rain spells", "rain squall", "rain storm", "moderate rain", "big rain storm", "very heavy rain") ~ "rain",
weather_condition %in% c("little snow", "snow sleet", "light snow", "thick snow", "pleasant snow", "snowy", "snow", "snow spells", "snow squall", "big snow storm", "snow storm", "snowing", "snowing fast", "moderate snow") ~ "snow",
weather_condition %in% c("stormy", "tough storm", "very heavy storm", "moderate rainstorm", "heavy storm") ~ "storm",
weather_condition %in% c("thunder", "heavy thunder") ~ "thunder",
weather_condition %in% c("sharp lightning", "lightning") ~ "lightning",
weather_condition %in% c("misty", "good weather", "moderate weather", "sun out", "hail") ~ "other",
))weather_con <- rbind(weather_con_1873_long, weather_con_1874_long)Wind Speed and Weather Conditions
Categories might need to be redefined and given levels to be more meaningful
#currently only have the 1873 weather
wind_weather <- full_join(combined_wind, weather_con, by = c("date_mdy", "month", "year", "period"))## Warning in full_join(combined_wind, weather_con, by = c("date_mdy", "month", : Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 104 of `x` matches multiple rows in `y`.
## ℹ Row 525 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
## "many-to-many"` to silence this warning.wind_weather <- wind_weather %>%
mutate(wind_direction = ifelse(wind_direction == "NA", NA, wind_direction)) %>%
mutate(wind_speed = ifelse(wind_speed == "NA", NA, wind_speed )) %>%
mutate(weather_condition = ifelse(weather_condition == "NA", NA, weather_condition )) %>%
drop_na(wind_direction, wind_speed, weather_condition)freq_wind_weather <- wind_weather %>%
group_by(wind_speed,
weather_condition) %>%
summarize(frequency = n())## `summarise()` has grouped output by 'wind_speed'. You can override using the
## `.groups` argument.freq_wind_weather %>%
ggplot(aes(x = weather_condition,
y = wind_speed,
fill = frequency)) +
geom_tile() +
scale_fill_viridis_c() +
labs(title = "Wind Speed and Weather Conditions Heat Map",
x = "Weather Condition",
y = "Wind Speed",
fill = "Frequency") +
coord_flip() +
scale_fill_viridis_c(direction = -1) #for darker = higehr frequency## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
wind_weather %>%
group_by(wind_speed,
weather_condition,
year) %>%
summarize(frequency = n()) %>%
ggplot(aes(x = weather_condition,
y = wind_speed,
fill = frequency)) +
geom_tile() +
scale_fill_viridis_c() +
labs(title = "Wind Speed and Weather Conditions by Year Heat Map",
x = "Weather Condition",
y = "Wind Speed",
fill = "Frequency") +
facet_wrap(~ year, nrow = 2) +
coord_flip() +
scale_fill_viridis_c(direction = -1) #for darker = higehr frequency## `summarise()` has grouped output by 'wind_speed', 'weather_condition'. You can
## override using the `.groups` argument.
## Scale for fill is already present. Adding another scale for fill, which will
## replace the existing scale.
Letters
all journals should be modified to change the “read” as “received” and “read and write” to “received and write” for the letter column
journal_1873 <- journal_1873 %>%
mutate(letter = case_when(
letter == "read" ~ "received",
letter == "read and write" ~ "received and write",
TRUE ~ letter # Keep the original value if none of the conditions match
))
journal_1874 <- journal_1874 %>%
mutate(letter = case_when(
letter == "read" ~ "received",
letter == "read and write" ~ "received and write",
TRUE ~ letter # Keep the original value if none of the conditions match
))letters_1873 <- journal_1873 %>%
select(date_mdy, month, journal_entry, letter, letter_from, letter_to, notes, ) %>%
mutate(year = "1873")
letters_1874 <- journal_1874 %>%
select(date_mdy, month, journal_entry, letter,letter_from, letter_to, notes) %>%
mutate(year = "1874")combined_letters <- rbind(letters_1873, letters_1874)combined_letters %>%
count(letter, year) %>%
mutate(letter = ifelse(letter == "NA", NA, letter)) %>%
drop_na(letter) %>%
ggplot(aes(x = letter,
y = n,
fill = year)) +
geom_bar(stat = "identity", position = "dodge") +
facet_wrap(~ year) +
labs(title = "Letter Communication",
subtitle = "For 1873 and 1874",
x = "Letter Status",
y = "Frequency",
fill = "Year")
combined_letters %>%
mutate(letter = ifelse(letter == "NA", NA, letter)) %>%
drop_na(letter) %>%
filter(letter != "no letter") %>%
count(letter, year) %>%
ggplot(aes(x = letter, y = n, fill = year)) +
geom_bar(stat = "identity", position = "dodge") +
facet_wrap(~ year) +
labs(title = "Frequency of Letter Communication Mentioned",
subtitle = "For 1873 and 1874",
x = "Letter Status",
y = "Frequency",
fill = "Year") +
scale_y_continuous(breaks = seq(5, 30, 5))
month_order <- c("January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December")
combined_letters %>%
mutate(letter = ifelse(letter == "NA", NA, letter)) %>%
drop_na(letter) %>%
filter(letter != "no letter") %>%
mutate(month = factor(month, levels = month_order)) %>%
count(letter, year, month) %>%
ggplot(aes(x = letter, y = n, fill = year)) +
geom_bar(stat = "identity", position = "dodge") +
facet_wrap(~ year) +
labs(title = "Frequency of Letter Communication Mentioned per Month",
subtitle = "For 1873 and 1874",
x = "Letter Status",
y = "Frequency",
fill = "Year") +
facet_wrap(~ month)
Communication (Names & Letters)
There are still issues with a person being mentioned trough different names or with different punctuations for initials. The current journals have been updated since these visualizations were made, but it will be critical to keep an eye out for the variations when visualizing these.
#only rows with letters written or received
communication <- combined_letters %>%
mutate(letter = ifelse(letter == "NA", NA, letter)) %>%
drop_na(letter) %>%
filter(letter != "no letter") %>%
separate_rows(letter_from, sep = ", ") %>%
separate_rows(letter_to, sep = ", ") %>%
mutate(letter_from = case_when(
letter_from %in% c("Perlley and Russel", "Perley and Russell") ~ "Perley and Russell",
letter_from %in% c("G.L. Hodgkins", "G. L. Hodgkins", "G. L Hodgins") ~ "G. L. Hodgkins",
letter_from %in% c("Charles C. Burrill", "C.C. Burrill", "C. C. Burrill", "C.C Burrill", "C.C Burill") ~ "C. C. Burrill",
letter_from %in% c("Benj Kittridge", "Benj. Kittridge") ~ "Benj. Kittridge",
letter_from %in% c("Reg. of Deeds", "Reg of Deed") ~ "Reg of Deeds",
TRUE ~ letter_from)) %>%
mutate(letter_to = case_when(
letter_to %in% c("C. C. Burrill", "C.C Burrill") ~ "C. C. Burrill",
letter_to %in% c("Benj. Kittrige", "Benj. Kittridge") ~ "Benj. Kittridge",
TRUE ~ letter_to))communication %>%
select(letter_from, month, year) %>%
count(letter_from, month, year) %>%
drop_na(letter_from) %>%
group_by(month, letter_from) %>%
mutate(last_word = str_extract_all(letter_from, "\\S+") %>% map_chr(tail, 1)) %>%
arrange(last_word) %>% # to arrange by alphabetical order based on last word
ggplot(aes(x = factor(letter_from, levels = unique(letter_from)),
y = n,
fill = month)) +
geom_bar(stat = "identity") +
theme_minimal() +
labs(title = "Frequency of Letters Received by Freeland Bunker",
subtitle = "In 1873 and 1874",
x = "People Sending the Letters",
y = "Frequency",
fill = "Month") +
scale_fill_discrete(labels = c("January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December")) +
coord_flip() +
facet_wrap(~ year)
communication %>%
select(letter_to, month, year) %>%
count(letter_to, month, year) %>%
drop_na(letter_to) %>%
group_by(month, letter_to, year) %>%
mutate(last_word = str_extract_all(letter_to, "\\S+") %>% map_chr(tail, 1)) %>%
arrange(last_word) %>% #to arrange by alphabetical order
ggplot(aes(x = factor(letter_to, levels = unique(letter_to)),
y = n,
fill = month)) +
geom_bar(stat = "identity") +
theme_minimal() +
labs(title = "Frequency of Letters Written by Freeland Bunker",
subtitle = "In 1873 and 1874",
x = "Letter Recipients",
y = "Frequency",
fill = "Month") +
scale_fill_discrete(labels = c("January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December")) +
coord_flip() +
facet_wrap(~ year)
Boats
Boats like the A. G. Brooks and Sea Flower, notably are not currently being picked up and displayed in the word cloud. More thoughts need to be given to create a foolpoof functions that could pick out all boats mentioned. N grams wil probably more appropriate than bigram.
#this is is from master_journal_lists as of 08/2023 --> it will keep growing so more thoughts need to be put in ways to keep track of the progression of the lists
boats <- c("Signal", "Amulet", "Black Warrior", "Sea Flower", "A.G. Brooks", "Henrietta", "Mermaid", "Harp", "Woodcock", "Roamer", "Virgin", "Neptune", "Banner", "Old Chad", "Sea Pigeon", "Elizabeth", "Wyoming", "Mary and Eliza", "Mars Hill", "Nauseag", "Washington", "Lebanon")boats_lower <- tolower(boats)
journal_entry_ngram <- combined_journals %>%
unnest_tokens(bigram, journal_entry, token = "ngrams", n = 2)
journal_entry_ngram_lower <- journal_entry_ngram %>%
mutate(bigram = tolower(bigram))
bigram_counts_lower <- journal_entry_ngram_lower %>%
separate(bigram, c("word1", "word2"), sep = " ") %>%
filter(
(word1 %in% boats_lower & !word2 %in% stop_words$word) |
(word2 %in% boats_lower & !word1 %in% stop_words$word)
) %>%
count(word1, word2, sort = TRUE)
boats_mentions_filtered <- bigram_counts_lower %>%
filter(word1 %in% boats_lower | word2 %in% boats_lower)
boats_freq <- bind_rows(
data.frame(word = boats_mentions_filtered$word1, freq = boats_mentions_filtered$n),
data.frame(word = boats_mentions_filtered$word2, freq = boats_mentions_filtered$n)
)
boats_freq <- boats_freq %>%
count(word)
boats_freq <- boats_freq %>%
filter(word %in% boats_lower)
boats_freq$word <- tools::toTitleCase(boats_freq$word)
wordcloud2(boats_freq, size = 1, color = "random-light", backgroundColor = "black", minSize = 1, minRotation = 0)